Cost-conscious classifier ensembles

Ensemble methods improve the classification accuracy at the expense of testing complexity, resulting in increased computational costs in real-world applications. Developing a utility-based framework, we construct two novel cost-conscious ensembles; the first one determines a subset of classifiers and the second dynamically selects a single classifier. Both ensembles successfully switch between classifiers according to the accuracy-cost trade-off of an application.